CADKnitter is a compositional CAD generation framework that uses geometric-guiding cues to steer diffusion sampling, enabling the creation of complementary CAD parts that satisfy both geometric constraints of an existing model and semantic constraints from a text prompt. The authors introduce KnitCAD, a dataset of over 310,000 CAD models paired with textual prompts and assembly metadata to support training and evaluation. Experiments show that CADKnitter outperforms state‑of‑the‑art baselines by a clear margin.
By Tri Le, Khang Nguyen, Baoru Huang, Tung D. Ta, Anh Nguyen
arXiv:2607. 04119v1 Announce Type: cross Abstract: Reconstructing Computer-Aided Design (CAD) modeling sequences from images is crucial for preserving design intent and supporting parametric editing.
By Zhaopeng Feng, Chen Zhi, Xuhong Zhang, Zhengwen Feng, Xinkui Zhao
arXiv:2606. 05058v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models.
By Jingyuan Chen, Sheng Jin, Haopeng Sun, Wentao Liu, Chen Qian
arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.
By Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
By Xiaoyu Dong, Zhi Li, Xiao-Ming Wu
arXiv:2606. 30429v1 Announce Type: new Abstract: Text-to-3D systems can now synthesize a mechanical part from a single sentence, yet the result is a shape to render, not a design to edit.
By Liang Wang, Zhaoyang Xi, Zekai Xiang, Heng Meng, Qishan Zhang, Pingyi Zhou, Jin Liu, Litao Chen
arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.
By Fumin Liu, Haoyu Zhou, Fei Hao, Lin Yang
CADFather is an autonomous system that reconstructs editable CAD models from 3D meshes by coordinating vision‑language assistants, learned and algorithmic tools, and numerical optimization. It evaluates and refines candidate CAD programs, maintaining alternatives for each part and preserving the best valid result throughout reconstruction. The system uses pretrained models without additional training and is evaluated on multiple benchmark datasets for reconstruction quality and execution validity.
By Gennadiy Savrasov, Maksim Elistratov, Nikita Gavrilov, Albert Garifullin, Oleg Pavlov, Soslan Kabisov, Vladimir Frolov, Anton Konushin, Andrey Kuznetsov, Dmitrii Zhemchuzhnikov
VisCAD is a foundation model suite that tackles AI-assisted computer-aided design for industrial products, covering both part-level and assembly-level generation. Its core component, VisCAD‑M1, is a 27B model trained for part-level design generation and outperforms existing models on PubCADBench and RealCADBench, achieving a part-level score of 0.5540 and reaching 0.5797 when used as a test-time verifier. VisCAD also offers a domain-specific harness that improves complex assembly generation compared to general-purpose harnesses, showing quantitative and qualitative advantages.
By JoyIndustrial VisCAD Team, Linxin Cai, Qiuhe Hong, Zhichao Huang, Guanlin Li, Hongsen Liu, Ziqi Liu, Yichen Long, Luya Wang, Yuchen Wang, Wenxiang Wu, Huimu Yu, Ning Zhang
Reconstructing an editable CAD model from a 3D shape remains a challenging engineering task. Existing methods can propose CAD operations, but no single source of proposals works equally well across di...
The paper introduces LASP, a framework that uses large language models to generate structured B‑spline priors from procedural modeling histories. By translating design intent into rich textual descriptions, LASP provides semantic reasoning that guides conventional CAD solvers toward more accurate and coherent surface fitting. Experiments show that language‑driven priors outperform traditional machine learning approaches, establishing a new paradigm for language‑guided geometric optimization.
By Yunzhong Lou, Yusheng Luo, Jiahao Li, Yu Song, Xiangdong Zhou
arXiv:2606. 13368v1 Announce Type: new Abstract: Computer-Aided Design is pivotal in modern manufacturing, yet existing automated methods predominantly rely on open-loop, one-shot generation, creating a mismatch with iterative real-world practices.
By Tao Hu, Jiaxin Ai, Licheng Wen, Xueheng Li, Shu Zou, Siqi Li, Nianchen Deng, Xinyu Cai, Hongbin Zhou, Pinlong Cai, Daocheng Fu, Yu Yang, Hairong Zhang, Botian Shi, Xuemeng Yang